An ultra-sensitive impedimetric immunosensor for detection of the serum oncomarker CA-125 in ovarian cancer patients
Bibliographic record
Abstract
Effective treatment of ovarian cancer depends upon the early detection of the malignancy. Here, we report on the development of a new nanostructured immunosensor for early detection of cancer antigen 125 (CA-125). A gold electrode was modified with mercaptopropionic acid (MPA), and then consecutively conjugated with silica coated gold nanoparticles (AuNP@SiO2), CdSe quantum dots (QDs) and anti-CA-125 monoclonal antibody (mAb). The engineered MPA|AuNP@SiO2|QD|mAb immunosensor was characterised using transmission electron microscopy (TEM), atomic force microscopy (AFM), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Successive conjugation of AuNP@SiO2, CdSe QD and anti-CA-125 mAb onto the gold electrode resulted in sensitive detection of CA-125 with a limit of detection (LOD) of 0.0016 U mL(-1) and a linear detection range (LDR) of 0-0.1 U mL(-1). Based on the high sensitivity and specificity of the immunosensor, we propose this highly stable and reproducible biosensor for the early detection of CA-125.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".